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COMPUTER - ASSISTED INFUSION OF MUSCLE RELAXANTS

COMPUTER - ASSISTED INFUSION OF MUSCLE RELAXANTS. Dr Valérie BILLARD. NEUROMUSCULAR BLOCKERS (NMB) : EXPECTED EFFECTS. Required : . larynx abdominal orthopedic eye, neuro. EFFECT NM blockade. NMB - drug ? -dosage ?. Unexpected - early motor testing - respiratory failure

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COMPUTER - ASSISTED INFUSION OF MUSCLE RELAXANTS

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  1. COMPUTER - ASSISTED INFUSION OF MUSCLE RELAXANTS Dr Valérie BILLARD

  2. NEUROMUSCULAR BLOCKERS (NMB) : EXPECTED EFFECTS Required : larynx abdominal orthopedic eye, neuro... EFFECT NM blockade NMB - drug ? -dosage ? Unexpected - early motor testing - respiratory failure - light anaesthesia

  3. NMB : MEASURED EFFECTS Twitch TOF Tetanos DBS T1/Tinitial T4/T1 PTC NMB - drug ? -dosage ? Muscle (AP, OO) ? Expected effect Visual Force transducer Accelerometry EMG

  4. NMB : Simple closed-loop systems ANESTHESIOLOGIST PATIENT MONITOR : INFUSION DEVICE CONTROLLER

  5. Simple closed loop systems :the controller • Properties • Output dependent on the control opération • rapidly achieve a stable control • protected from electrical interference and noise • easy to monitor and to operate • Principle based upon the error (e= measured - target) • Proportional : Rate = K . Weight . e • Proportional Integral: Rate = Kp.weight.e + Ki.weight.(Se+P) • Proportional Integral Derivative: Rate = K1.e+K2.Se+K3.de/dt

  6. dE/dt setpoint E - + Fuzzifier Fuzzy control Defuziffier Process Fuzzy logic control • Control accepting qualitative data as «small»,«big»... • Input = error E and change in the error • Output = controller or change in the controller • Ex. «IF error = 0 and change in error is positive small, THEN output is negative small ».

  7. Reference Drug Measure Controller Error (mean) Webster 1987 atracurium EMG P.I.D. 3% Webster 1987 " Force/EMG P.I.D. 11% Mc Leod 1989 " EMG P.I. 1.3% O'Hara 1991 " EMG P.I.D. 8.5-13% Assef 1993 Atra /Vecu EMG/accelero P. 10-50% Stinson 1994 atracurium accelerometry P. negl. Ross 1997 atracurium EMG Fuzzy 0.5% Closed loop systems : the performances

  8. FROM THE DOSE TO THE EFFECT : PK -PD RELATIONSHIP NMB DOSE EFFECT (predicted) PK PD CONCENTRATION (plasma) CONCENTRATION (effect site) Ke0

  9. TARGET THE « MEASURABLE » PREDICTED EFFECT USING PKPD NMB DOSE EFFECT (predicted) PK PD CONCENTRATION (plasma) CONCENTRATION (effect site) Ke0

  10. PK -PD RELATIONSHIP : PERFORMANCES EFFECT (measured) ERROR EFFECT (predicted) NMB DOSE PK PD CONCENTRATION (plasma) CONCENTRATION (effect site) Ke0

  11. PKPD MODEL : ERROR ON THE PK • Wrong drug (rare!) • Wrong model (elimination from central compartment) • PK parameters not adjusted to the current patient • Age • elderly (CL1æ Vdss ä) • infants (CL1 and Vdss ä) • Obesity (ideal weight vs. real weight) • Renal or liver failure • Variability

  12. PKPD MODEL : ERROR ON THE PD • PD model inadapted : • Emax vs. others ? • other muscle or measure than in the model • PD parameters not adjusted to the patient • Age : EC50 lower in infants • Burning • Interactions (volatile +++) • Wrong Ke0 : hypothermia, age • Variability

  13. HOW TO DECREASE THE ERROR? • Adjust the PK and PD model to covariates • Clinical research and publications • Library of models • Enter a measured value to adjust the model : Bayesian forecasting • Take globally account of the patient covariates • Could change over time

  14. FROM THE DOSE TO THE EFFECT : PK -PD RELATIONSHIP EFFECT (measured) EFFECT (predicted) NMB DOSE PD PK CONCENTRATION (plasma) CONCENTRATION (effect site) Ke0

  15. Bayesian approach • Comes from Bayes description of conditional probability • Combines : • the amount of information given by a population model • with 1 or few pieces of information coming from a patient • to improve the accuracy of the model to describe this patient • Has been used mainly by adding a measured concentration to PK model and applied to antibiotics, lidocaine, theophylline, antineaplasic agents,...

  16. Bayesian adaptation using Stanpump • Available for atracurium, vecuronium, rocuronium • Only for target blockade less than 95% • Adjust the PK model to a measured value of effect • This value is entered manually (open loop) • Then adjust the target in order to have minimal change

  17. CONCLUSION • The effects of muscle relaxants could be measured • This measured effect can • act as input in closed loop system where output is dose • become a target for CCI based on PKPD model • be compared to the target to adapt the model to the patient • PK model : mainly interindividual variability • PD model : mainly intraindividual variability • The relevant clinical effects corresponding to these measures remain to be known

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